Control, adaptive

Controllers inevitably require tuning of the controller settings to achieve a satisfactory degree of control if the process operating conditions or the environment changes significantly, e.g. because of heat exchanger 

Measurements of the controlled and the manipulated variables are used to estimate the parameters of a simple process model. This process model is then used to calculate the new control parameters based on a pre-selected tuning method. 

Adaptive control has been an active area of research for many years. The fullblown ideal adaptive controller continuously identifies (on-line) the parameters of the process as they change, and retunes the controller appropriately. Unfortunately, this on-line adaptation is fairly complex and has some pitfalls that can lead to poor performance. Also, it takes considerable time for the on-line identification to be achieved, which means that the plant may have already changed to a different condition. These are some of the reasons why on-line adaptive controllers are mot widely used in the chemical industry. 

However, the main reason for the lack of wide application of on-line adaptive control is the lack of economic incentive. On-line identification is rarely required because it is usually possible to predict with off-line tests how the controller must be retuned as conditions vary. The dynamics of the process are determined at different operating conditions, and appropriate controller settings are determined for all the different conditions. Then, when the process moves from one operating region to another, the controller settings are automatically changed. This is called openloop-adaptive control or atn scheduling. 

These openloop-adaptive controllers are really just another form of nonlinear control. They have been quite successfully used in many industrial processes, particularly in batch processes where operating conditions can vary widely. 

The one notable case where on-line adaptive control has been widely used is in pH control. The wide variations in titration curves as changes in buffering occur makes pH control ideal for on-line adaptive control methods. 

Several instrument vendors have developed commercial on-line adaptive controllers. Difficulties have been reported in two situations. First, when they are applied in a multivariable environment, the interaction among control loops can cause the adaptation to fail. Second, when few disturbances are occurring, the adaptive controller has little to work with and its performance may degrade drastically. 

An adaptive control system can automatically modify its behaviour according to the changes in the system dynamics and disturbances. They are applied especially to systems with non-linear and unsteady characteristics. There are a number of actual adaptive control systems. Programmed or scheduled adaptive control uses an auxiliary measured variable to identify different process phases for which the control parameters can be either programmed or scheduled. The best values of these parameters for each process state must be known a priori. Sometimes adaptive controllers are used to optimise two or more process outputs, by measuring the outputs and fitting the data with empirical functions. 

A system or controller which can adjust its parameters automatically in such a way as to compensate for variations in the characteristics of the process it controls is termed an adaptive system or adaptive controller. 

The concept of adaptive control, in that it can be considered as a kind of non-linear feedback action, is not new. For example, Kalman 45 described a self-optimising controller in 1958. However, it was impossible to implement Kalman s procedure at that time due to digital computer limitations. The more recent 

Adaptive controllers can be usefully applied because most processes are nonlinear (Section 7.16) and common controller design criteria (Section 7.12) are based on linear models. Due to process non-linearities, the controller parameters required to give the desired response of the controlled variable change as the process steady state alters. Furthermore, the characteristics of many processes vary with time, e.g. due to catalyst decay, fouling of heat exchangers, etc. This leads to a deterioration in the performance of controllers designed upon a linear basis. 

Generally, an objective function is required for the adaptation strategy which guides the adaptation mechanism to produce the best settings of the controller parameters. For example, the A decay ratio specification could be employed, or the ISE criterion (Section 7.11). For instance, if the A decay ratio criterion is used, then, if any change in process parameters leads to decay ratios other than A, the adaptation mechanism adjusts the controller parameters until a A decay ratio in the controlled response is achieved once again. 

AstrOm and WlTTENMARK 46 have listed three basic schemes that have been devised for the adaptive control of processes, viz.  

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Adaptive Control. An adaptive control strategy is one in which the controller characteristics, ie, the algorithm or the control parameters within it, are automatically adjusted for changes in the dynamic characteristics of the process itself (34). The incentives for an adaptive control strategy generally arise from two factors common in many process plants (/) the process and portions thereof are really nonlinear and (2) the process state, environment, and equipment s performance all vary over time. Because of these factors, the process gain and process time constants vary with process conditions, eg, flow rates and temperatures, and over time. Often such variations do not cause an unacceptable problem. In some instances, however, these variations do cause deterioration in control performance, and the controllers need to be retuned for the different conditions. 

K. J. Astrom and B. SUixtemn.2iik, Adaptive Control Systems, Addison-Wesley, Publishing Co., Inc., Reading, Mass., 1988. 

While the single-loop PID controller is satisfactoiy in many process apphcations, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods hereafter that can be implemented via computer control, namely feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. 

Adaptive Control Process control problems inevitably require on-hne tuning of the controller constants to achieve a satisfactory degree of control. If the process operating conditions or the environment changes significantly, the controller may have to be retuned. If these changes occur quite frequently, then adaptive control techniques should be considered. An adaptive control system is one in which the controller parameters are adjusted automatically to compensate for changing process conditions. 

During the 1980s, several adaptive controllers were field-tested and commerciahzed in the U.S. and abroad, including products by ASEA (Sweden), Leeds and Northrup, Foxboro, and Sattcontrol. At the present time, some form of adaptive tuning is available on almost all PID controllers. The ASEA adaptive controller, Novatune, was... 

The subject of adaptive control is one of current interest. New algorithms are presently under development, but these need to be field-tested before industrial acceptance can be expected. It is clear, however, that digital computers will be required for implementation of self-adaptive controllers due to their complexity. An adaptive controller is inherently nonlinear and therefore more complicated than the conventional PID controller. 

Astrom, K.J. and Wittenmark, B. (1989) Adaptive Control, Addison-Wesley, Reading, Mass. 

The derivation of process models for adaptive control falls exactly within the framework of the estimation problem studied in this chapter. Control-related implementation are natural extensions to the current work and are... 

The correct interpretation of measured process data is essential for the satisfactory execution of many computer-aided, intelligent decision support systems that modern processing plants require. In supervisory control, detection and diagnosis of faults, adaptive control, product quality control, and recovery from large operational deviations, determining the mapping from process trends to operational conditions is the pivotal task. Plant operators skilled in the extraction of real-time patterns of process data and the identification of distinguishing features in process trends, can form a mental model on the operational status and its anticipated evolution in time. 

It may be useful to point out a few topics that go beyond a first course in control. With certain processes, we cannot take data continuously, but rather in certain selected slow intervals (c.f. titration in freshmen chemistry). These are called sampled-data systems. With computers, the analysis evolves into a new area of its own—discrete-time or digital control systems. Here, differential equations and Laplace transform do not work anymore. The mathematical techniques to handle discrete-time systems are difference equations and z-transform. Furthermore, there are multivariable and state space control, which we will encounter a brief introduction. Beyond the introductory level are optimal control, nonlinear control, adaptive control, stochastic control, and fuzzy logic control. Do not lose the perspective that control is an immense field. Classical control appears insignificant, but we have to start some where and onward we crawl. 

Tada, J., Kono, T., Suda, A., Mizuno, H., Miyawaki, A., Midorikawa, K. and Kannari, F. (2007). Adaptively controlled supercontinuum pulse from a micro structure fiber for two-photon excited fluorescence microscopy. Appl. Opt. 46, 3023-30. 

Rusnak, I. A. Guez and I. Bar-Kana. Multiple Objective Approach to Adaptive Control of Linear Systems. In Proceedings of the American Control Conference. San Francisco, pp. 1101-1105 (1993). 

Adams-Nickerson color space, 7 320 Adapalene, 25 789 Adapress, molecular formula and structure, 5 128t Adaptive control system, 20 698 Adaptive sampling techniques, 26 1016-1019... 

No degree of sophistication in the control system (be it adaptive control, Kalman filters, expert systems, etc.) will work if you do not know how your process works. Many people have tried to use complex controllers to overcome ignorance about the process fundamentals, and they have failed Learn how the process works before you start designing its control system. 

Seborg, Edgar, and Shah (A/ChE Journal 1986, Vol. 32, p, 881) give a survey of adaptive control strategies in process control. 

A very popular sequence of inputs is the pseudorandom binary sequence (PRBS). It is easy to generate and has some attractive statistical properties. See System Identification For Self-Adaptive Control, W. D. T. Davies, London, Wiley-Iflterscience, 1970. 

G. Bastin and D. Dochain. On-Line Estimation and Adaptive Control of Bioreactors. Elsevier, Amsterdam, 1990. 

D. Dochain and M. Perrier. Advanced Instrumentation, Data Interpretation, and Control of Biotechnological Processes, chapter Monitoring and Adaptive Control of Bioprocesses, pages 347-400. Kluwer Academic Publishers, 1998. 

M.J. Arauzo-Bravo, J.M. Cano-Izquierdo, E. Gomez-Sanchez, M.J. Lopez-Nieto, Y.A. Dimitraidis and J. Lopez-Coronado, Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems. Control Eng. Pract., 12, 1073-1090 (2004). 

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